Predictive Information Retrieval

ABSTRACT

A computer-implemented method for generating results for a client-requested query involves receiving a query produced by a client communication device, generating a result for the query in response to reception of the query, determining one or more predictive follow-up requests before receiving an actual follow-up request from the client device, and initiating retrieval of information associated with the one or more predictive follow-up requests, and transmitting at least part of the result to the client device, and then transmitting to the client device at least part of the information associated with the one or more predictive follow-up requests.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of and claims priority toU.S. application Ser. No. 14/281,994, filed on May 20, 2014, which is acontinuation of U.S. application Ser. No. 13/615,791 (now U.S. Pat. No.8,768,958), filed on Sep. 14, 2012, which is a continuation of U.S.application Ser. No. 11/004,499, filed on Dec. 3, 2004.

TECHNICAL FIELD

The invention relates to assisting users of computing or communicationdevices in obtaining information, and more particularly to providinginformation quickly even where the communication medium is bandwidthlimited.

BACKGROUND

As the speed of computing devices has increased, so have the demandsthat users of the devices place on them. While simple textualpresentation was acceptable in the past, users now expect full-color,and full-motion data. Such content delivery is relatively simple formoving data from local storage (e.g., a hard disk drive) on a machine tothe display apparatus of the machine, such as over a local bus.

Data transfer between heterogenous devices or over data paths havingconstrained bandwidth is much more complicated. Because the devices areheterogenous, or not closely matched, they generally must communicateover a standardized industry-wide protocol that may include featuresthat degrade possible performance in an effort to permitinteroperability. In a wired environment, induction within long wirescan interfere with a clean transfer of digital signals. In a wirelessenvironment, even more difficulties arise, and include limited allowablebandwidth which limits the amount of information that can be transferredeven under theoretically perfect conditions. Moreover, interference(e.g., from other broadcasting devices or from ambient conditions suchas solar interference) can degrade the bandwidth of the wirelesscommunication channels. As a result, data transmission, and particularlywireless transmission (such as through 802.11 or cellular networktransmission) always seems to lag user demand by a large amount. Inaddition to slow transmission speeds, certain networks, such as cellulartelephone networks, require initial connection times that add even moredelay.

Slow transmission speeds can be particularly annoying for real-timeoperations such as on-line searching and browsing on the internet. Inparticular, for such searching and browsing, information flow is highlyassymetrical, as user requests are small, but data transfer down to theuser is very large.

For example, a user might click on a link so as to send a short internetaddress to a server, and may be provided a very long response thatincludes color graphics and applets for a web page. The user may alsoperform a search and be provided with a number of search results(preferably in a form that does not require much bandwidth, such as thestandard Google search pages). The user may then want to access the webpages for each of the search results until the desired result islocated. However, for a device such as a cellular telephone that mightnot have adequate bandwidth, the user may be forced to spend as muchtime or more time waiting for information as the user spends reviewingthe information.

Thus, there is a need for a system that can provide a user with moreimmediate access to requested data even over a restricted communicationspath such as a wireless communications path.

SUMMARY

A computer-implemented method and system are disclosed to providepredictive information so that the information can be downloaded to auser's device before the user makes an explicit request for theinformation. As a result, the user will not need to wait for thedownload to occur once they make an explicit request for theinformation.

In one embodiment, a computer-implemented method for generating resultsfor a client-requested query is disclosed. The method comprisesreceiving a query produced by a client communication device, generatinga result for the query in response to reception of the query,determining one or more predictive follow-up requests before receivingan actual follow-up request from the client device, and initiatingretrieval of information associated with the one or more predictivefollow-up requests. The method may also involve transmitting at leastpart of the result to the client device, and then transmitting to theclient device at least part of the information associated with the oneor more predictive follow-up requests. The query may comprise a searchrequest, and the result may comprise data for generating a plurality ofhyperlinks to information relating to the query. The predictivefollow-up requests may also be determined by identifying the targets ofthe hyperlinks, or by identifying searching services available to theuser. The searching services may include web searching, shoppingsearching, news searching, or image searching, or a combination of thoseservices.

In some embodiments, a pre-established number of predictive follow-uprequests is determined. Also, a maximum transfer size may be computed asa function of a connection speed (predicted or measured) for the clientcommunication device, and information associated with the one or morepredictive follow-up requests will not be transmitted or itstransmission will be delayed relative to other predictive follow-uprequests if it exceeds the maximum transfer size. The predictivefollow-up requests may also be determined as a function of search termcorrelation for a plurality of clients.

In another embodiment, advertising matches for the predictive follow-uprequests may be identified before an actual follow-up request from theclient device is received.

In yet another embodiment, a computer-based data collection anddistribution system is disclosed, and comprises a request processor toreceive data requests from one or more remote clients, a responsegenerator to identify and present information responsive to the datarequests, a request predicting module to determine one or morepredictive follow-up requests representative of likely requests to bemade subsequently by a user of the remote client, and an interface toprovide the information responsive to the data requests and informationresponsive to the one or more predictive follow-up requests without theneed for an actual follow-up request. The response generator maycomprise an internet search engine, and the information responsive tothe data requests may comprise hyperlink information.

In some embodiments, the predictive follow-up requests may compriseinformation at locations associated with the hyperlink information, andthe predictive follow-up requests may comprise requests for a pluralityof search types, such as web search, news search, shopping search, imagesearch, blog search, or news group search. In addition, an advertisingselector may be provided to identify advertising content associated withthe predictive follow-up requests before a follow-up request is made.

In yet another embodiment, a computer-implemented data collection anddistribution system is disclosed and comprises a request processor toreceive data requests from one or more remote clients, a responsegenerator to identify and present information responsive to the datarequests, means for determining one or more predictive follow-uprequests representative of likely requests to be made subsequently by auser of the remote client, and an interface to provide the informationresponsive to the data requests and information responsive to the one ormore predictive follow-up requests without the need for an actualfollow-up request.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

These and other aspects will now be described in detail with referenceto the following drawings.

FIG. 1 shows schematically a data entry system having predictive dataflows between elements in the system.

FIG. 2 is a schematic diagram of a system to receive requests, generateresponses to the requests, and generate predictive responses for futurerequests.

FIG. 3 is a schematic diagram of a wireless communication handset forgenerating requests and receiving and using information generated inresponse to those requests.

FIG. 4 is a client-server flow chart showing exemplary steps forobtaining predictive information in response to a request.

FIG. 5 is a flow chart showing exemplary steps for providing predictiveinformation in response to a request.

FIG. 6 is a flow chart of exemplary steps for obtaining predictiveinformation relating to a request.

FIGS. 7a-7c are exemplary screen shots showing the operation ofpredictive information retrieval apparatuses.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The systems and techniques described here relate to assistance withinformation retrieval on a device having limited bandwidth. The systemscan take many forms, including personal communicators and personalcomputers. The data may also be entered in many forms, including bytelephone keypad and voice. In general, the systems operate byidentifying information that a user is likely to request in the future(predictive information), for example, based on requests the user hasmade in the past. The selection of predictive information may be basedonly on requests by the user, or on a combination of the requests andother information, such as recent activity by other users of thesystems.

Advantageously, the systems and techniques may allow users to reviewinformation while their devices are receiving or gathering additionalinformation in the background. In this manner, when the user is finishedreviewing the initial information and chooses to review additionalinformation, that additional information may have already beendownloaded to their device and they may not have to wait a long time forthe information to be presented. The predictive retrieval may be definedby the user so as to be personalized, and may also be made dynamic so asto match the user's likely preferences more closely.

FIG. 1 shows schematically a data entry system 10 having predictive dataflows between elements in the system. The system 10 includes a device12, shown as a cellular telephone handset for communicating with a user,but could take any appropriate form, such as a personal digitalassistant, a personal computer, or a voice-driven communication device.In this embodiment, the device 12 has a display screen 14 that can beprogrammed to display any appropriate information to a user of thedevice 12. For example, the display screen 14 could display informationrelated to an Internet searching application, such as a search box 14 aand related control buttons.

As shown, search box 14 a may simply be a contrasting area on the screenthat displays a search term as it is entered by the user. Search button14 b may submit the contents of search box 14 a to a search engine, suchas a search engine connected to the cellular network by the internet.The display screen 14 may then display the results of the search.Alternatively, “I'm Feeling Lucky” button 14 c may submit the searchresults, and the display screen 14 may then immediately display the pagefor the most appropriate match for the search.

Data may be entered into device 12 in any of a number of manners.Specifically, data entry keys 16 may be used to enter information suchas alphanumeric characters, and may take the form of a standardtelephone keypad, with alphabetic characters on the 2 through 9 keys.Other appropriate ways of entering alphanumeric information, includingvoice recognition, may also be used. As used here, references to entryof text encompass entry through a keyboard or by writing, and also othermanners of data entry, such as by speaking to a voice-recognitionenabled system.

In addition, control keys 17 may be provided, for example, to performpredetermined functions or programmed functions, or to control icons ondisplay screen 14. Control keys 17 may also be used as supplementalkeys, i.e., the number 0 could represent things other than 0, the “#”key may be used as a space key, the “*” key may be a proxy for thebackspace key, and the “1” key may represent punctuation.

Also, control wheel 15 may be provided on the side of the handset toallow a user to scroll through selections shown on display screen 14,and to push inward to click on a desired selection. Other appropriatedata presentation and data entry features may also be provided,particularly where the system 10 includes a full-sized personalcomputer, or where the system operates via voice control.

Device 12 may obtain the information the user needs through network 22,which may be a single network or combination of networks. Thus, forexample, device 12 could communicate through a cellular telephonenetwork using a WAP standard or other appropriate communicationsprotocol. The telephone network could in turn communicate with theinternet, either directly or through another network.

A search system 20 may also communicate with the network 22 so as toreceive requests from device 12 and locate information to return todevice 12. The search system 20 may take any applicable form, and maybe, for example, a system such as that provided by Google. Among othercomponents associated with search system 20 (which are described in moredetail with respect to FIG. 2), there is an index database 24 and acached information database 26. The index database 24 contains data thatrepresent searchable information available to the search system 20. Forexample, the search system 20 may scan the internet or intranets (fororganization-specific information) for content such as web sites orworkgroup discussions, may extract key words and other objects from thecontent, and may organize the information in a manner that permits readysearching. The index database 24 may also generate other informationfrom the content, such as indicators of how certain web sites link toother web sites, so as to allow for a system such as the Google PageRank system.

The cached information database 26 contains copies or substantial copiesof content that the system 20 locates. For example, the cachedinformation database 26 may contain web pages or portions of web pages(e.g., only textual, or only non-video content). In this manner, a userwho accesses system 20 may request the cached information rather thanmaking direct connection with the content provider, such as when thecontent provider is inaccessible, has changed the content since it wascached, or when the connection to the content provided is substantiallyslower than that to the search system 20.

The system 10 may also include other nodes 19, 21 connected to thenetwork 22. These nodes 19, 21 could include any sort of device ordevices capable of communicating with or over the network 22. Forexample, node 19 could be a web server that is capable of deliveringcontent in response to requests by users, such as a user of device 12.As another example, node 21 could be an e-mail server that may beaccessed by device 12, or could be a pass-through server for an e-maildelivery service (e.g., Research in Motion).

Lettered arrows in FIG. 1 show an exemplary flow of information relatingto the provision of a response to a request from device 12, along withthe provision of predictive information to device 12. Arrow A shows aninitial request sent by device 12, which in this example is a searchrequest. Although the request passes through network 22 (and othercomponents that are not pictured), it is directed at search system 20.Search system 20 then receives the request and applies it to a searchprocess, such as by parsing the request, locating content in indexdatabase 24 (see Arrow B1) that contains terms from the request, andranking the matches by some predetermined method such as the Page Rankmethod after receiving index information back from the index database 24(see Arrow C).

The search system 20 may then organize this information and transmit itback to the device 12 through network 22 as a search result (see ArrowD). For example, the returned information may include HTML code, XMLmessages, WAP code, Java applets, xhtml, plain text, voiceXML, VoxML,VXML, etc., that causes the device 12 to generate a display ofinformation such as descriptions of web sites that contain informationresponsive to the search, along with hyperlinks directed toward thosesites.

Before the user acts on the returned information, the system 10 may alsobegin gathering additional information that the user may request in thenear future. For example, the system 10 may, at or near the same time itis transmitting the information relating to the search results, analyzethose results to begin downloading information from the sites. Theinformation may be obtained from the cached information database 26(Arrows B2, E) or from nodes 19, 21 over network 22. As one example,where the search result is a list of web site addresses employed toprovide the user with hyperlinks, the system 10 may access those websites, gather information from them, and pass the information on todevice 12, where it can be stored until the user clicks on one of thelinks they were provided in response to the search request.

In this manner, the follow-up requests by the user can be responded toalmost instantly, as the information responsive to the request hasalready trickled into the memory of the device 12 while the user wasreviewing the results. As a result, the limited bandwidth available todevice 12 can be spread over time periods of non-use so that the usercan access information quickly from, e.g., local memory, and need notaccess it over a more constrained-bandwidth channel.

Other modes of predictive information retrieval may also be executed, inaddition to gathering information from web sites identified in responseto a search request. For example, where multiple search styles areassociated with each other, the results for a selected search style mayfirst be delivered, and the results for the other search styles may betransmitted in the background. The search styles may be, for example,the Google searches on the web, for images, for news, for newsgroups,for web log (blog) sites, or for product pricing and information (e.g.,Froogle). Other such information can include definitions of searchterms, telephone directory or mapping information, search results for alocalized area, video clips, audio clips, streaming files, televisionlistings, and blogs.

This approach is beneficial because users can be expected to selectother available search styles, or search services, after they havereviewed the initial search results. Also, a user might conduct theinitial search using the wrong style, and then upon discovering themistake, select a different search style. In these situations, thefollow up information would be more readily available to the user, suchas when the user selects the relevant on-screen tab for a differentsearch style. The results can thus be presented almost instantaneously,rather than making the user wait for additional connections and/ordownloading.

Predictive results may also be generated by associating the user'ssearch (or other) request with highly related requests. For example, thesystem 10 may look at statistics aggregated across multiple users, so asto determine, for example, that users who enter the search term “A”often also search for “X,” “Y,” and “Z.” For example, people who query“coffee” may frequently also query “Starbucks,” so when a particularuser enters “coffee” as a search, the system may pre-fetch results forthe search term “Starbucks” while the device is displaying to the userthe results of a search on “coffee.” Then, if the user acts consistentlywith his or her peer group, the follow-up information is nearlyimmediately accessible.

The follow-up information is generally stored in the memory of thedevice 12 for viewing if the user requests it. If a user does not end upviewing the follow-up information, it can be purged, for example, aftera pre-set time or after other information has been retrieved. Forexample, a portion of memory may be reserved for the predictiveinformation and may be treated as a first-in-first-out (FIFO) style ofmemory. The memory may also be purged each time a user conducts a newsearch or information request, or each time the user accesses aparticular application such as a search application.

The number of follow-up results to obtain can be determined in a numberof ways. For example, the number may be statically set, such as the topfive related queries. It can also be set as a function of the amount ofmemory in device 12, so that the memory does not overflow. In addition,it can be set as a function of predetermined or computed bandwidth ofthe device, so that the follow up information can be expected to bedownloaded to the device in a particular appropriate time period. Otherappropriate techniques for limiting the amount of follow up informationmay also be provided.

Where the follow up information includes information that would beretrieved from the user clicking on links in the search results, thedepth of retrieval may also be established. For example, the system maybe set to retrieve the information at the main web site, along with webpages that are one or two levels deep below the main web page. The depthof retrieval may also be a function of device 12 memory or systembandwidth. It could also be a function of the system's observed activityfor the user or group of users, such that a user who does not go deeplyinto sites will not be provided with much depth in the pre-fetchedresults, or a site for which visitors in the aggregate have not gonevery deep will also not have much depth for pre-fetched results.

Also, the system may prioritize the information retrieval, such that,for example, it retrieves text before graphics, or does not retrievegraphics at all as part of the pre-fetching operation to obtainpredictive information. In such a situation, the graphical informationcould be retrieved after the user actually selects the particular link.In this manner, the amount of pre-fetched information can be kept at aminimum, and the user can ultimately see all the information, but mayhave the text to review immediately (because it has been pre-fetched)while the graphics download.

The retrieval prioritization may also be addressed by search result. Forexample, if statistics indicate that 30% of users click on “Starbucks”when it is presented, and only 0.5% of users click on “CoffeeReview,”then the system could prioritize the retrieval of predictive informationfor Starbucks over that for CoffeeReview. As another example, lowbandwidth follow up information (such as newsgroup text) may betransmitted first, followed by more high bandwidth information (such asimages), so as to maximize the amount of useful information that ispre-fetched.

In addition, relevant advertising may also be downloaded either before,after, or interleaved with, the remaining downloaded information, andmay include, for example, results from an advertising program such asthe well known Google Adwords program. The identified advertisements maybe related to the search term, as is commonly performed. Alternatively,or in addition, the advertising may be generated, not on the searchitself, but on the predictive information generated by the system.

In this manner, the system may also deliver predictive advertising,either immediately so as to better anticipate the type of information asan input in which the user will be interested, or by pre-fetching theadvertising information so that it may be immediately displayed when theuser makes a new selection (e.g., when the pre-fetched follow-upinformation is displayed). Standard methods of identifying theappropriate advertising may be used, and may simply be passed thepredictive information rather than the initial information entered bythe user. The advertising could also be delivered, and/or displayed,apart from any predictive or follow-up information. In this manner, theadvertising that is delivered may be made more flexible and relevant tothe user, whether it is accompanied by predictive information retrievalor not.

The predictive information retrieval process may be accomplished in thesystem 10 by search system 20, by device 12, or by cooperation of searchsystem 20 and device 12. For example, device 12 may be programmed withcode that, when executed, analyzes results from a search request andthen begins accessing information from the sites identified by theresults while the user is still reviewing the results. Also, the device12 may be programmed with code that, when executed, conducts multiplesearch styles or types, returning results of the first search typeimmediately, and returning results of the other search types in thebackground while the user reviews the initial results.

The search system 20 may also take on the task of acquiring and managingfollow-up information, so that the device 12 need only receive theinformation, store it, and present it. For example, where the predictiveinformation retrieval mode relates to search styles (e.g., news,webgroups, blogs, images, and shopping), the search system 20 may be setto start delivering the follow-up results automatically because thesearch system 20 already has all the information that it needs in orderto do so.

In contrast, where the retrieval mode relates to acquisition of webcontent from target sites of a search result, the device 12 rather thanthe search system 20 may be a better candidate for the follow-upretrieval (so that the web content need not pass through the searchsystem 20, but may instead be acquired directly by the device 12). Also,the search system 20 could provide commands to the device 12 in such amanner that the device 12 acquires the follow up information directlyfrom third parties, but under the influence of search system 20.

The information flow shown in FIG. 1 may take place asynchronously, sothat, as the initial results are being transmitted to the device 12,additional predictive results are being located by system 10.Transmission of the predictive results may then begin, and acquisitionof additional predictive information may happen at the same time.Coordination of the information flows and passing of messages may occurin any appropriate and well known manner.

Follow-up actions by the user may also create an interrupt in the flowshown in FIG. 1. For example, a user may select a result from an initialinformation response while follow up information is still being gatheredby the device 12. In that situation, the user's action may interrupt theongoing download of predictive information (indicated by Arrow F). Thenew results for the second request may then begin downloading to thedevice 12, and the search system 20 can begin identifying and gatheringpredictive information for the second request. The search system 20 canthen begin downloading that new predictive information in a similarmanner to that for downloading the predictive information for the firstrequest.

The previously downloaded predictive information may be purged at thispoint, may be pushed out as necessary by the new information (such as ina FIFO system), or may be stored for later use. For example, whenpredictive information for the second request has fully downloaded, thesystem 10 may return to downloading the remaining predictive informationfor the first search. In this manner, if the user returns to theoriginal request (as users are known to do), their further selectionscan be made with immediate response from the system 10.

In addition, appropriate visual, audio, or tactile indicators may alsobe provided to the user as a guide to let the user know when follow upinformation has been loaded and they can request it and receive itimmediately, as is shown in more detail with respect to FIGS. 7a -7 c.

The system 10 can also be customized for a particular user, eitherindividually or as a member of a group. For example, a user could beprovided with options on device 12 or on another device such as apersonal computer running a browser application, with a template forselecting preferred parameters for the provision of predictiveinformation. For example, the user may establish the amount of memory tobe used to store predictive information on the device 12, may establisha default information retrieval mode, may set a preferred order fordownloading information (e.g., news before newgroups, or news beforeweb), may restrict the downloading of certain results such as shoppingresults, or may download news in order (e.g., chronologically orreverse-chronologically). This customization may also be provided as amuch broader customization session by which the user could set manyother parameters that affect the information retrieval experience forthe user when using device 12.

The customization may be made more automated through the use ofprofiles. For example, a teenage user may have no interest in news, buta very high interest in shopping, so news could be demoted and becollected last for their telephone. As a result, that person couldselect a “teen” profile that would cause a number of parameters to beset in a manner that is believed to be preferred by teens. The user maythen edit the pre-set parameters. Also, when the predictive informationis based on aggregated search statistics or correlation between a searchterm and other terms used by other users in similar circumstance, thestatistics for only a subgroup (e.g., teens, seniors, sports fanatics,etc.) may be analyzed. Also, an organization such as a business couldestablish a default profile (which could include items well beyondpredictive information retrieval parameters) for its employees, such asif the organization contracts with a wireless information provider tooutfit all its employees with a particular kind of device. Profilescould also be prepared for specified subsets of users (e.g., sales,executive, etc.)

Also, while the system 10 is shown as working with a wirelesscommunication device 12 such as a cellular telephone, it could providebenefits in many other environments, particularly where bandwidth islimited. For example, a wireless system installed in an automobile couldalso benefit from such predictive information retrieval. A personalcomputer or other form of computer, even with a supposed broadbandconnection, could also benefit where the information to be retrieved islarge relative to the amount of bandwidth. Even where the information isnot much larger than the available bandwidth, delays in gaining accessto information can be annoying to a user, and can interfere with theflow of the user's work, particularly if the delays are repeated manytimes. Thus, the predictive information retrieval as described can havebenefits in a broad array of applications.

Other benefits may be achieved in broadband environments. For example,news video clips may be predictively downloaded to a user based onpredictive selections by the system 10, and may include clips of variousrecent news items for the day. The user may then be able to watch all ofthe clips (or prepare an aggregate personalized newscast eitherautomatically or manually) without download delay.

FIG. 2 is a schematic diagram of a search system 20 to receive requests,generate responses to the requests, and generate predictive responsesfor future requests. Search system 20 is connected to a network 58, suchas the internet, so as to be able to communicate with users who may beinterested in accessing the services provided by search system 20.Search system 20 may be broken into multiple separate systems to allowfor scalability, and may be connected to network 58 in any of a varietyof ways, as is commonly known.

Search system 20 may also include an index database 24 and a cachedinformation database 26. These databases may be connected to searchsystem 20, for example, by a high bandwidth LAN or WAN, or could also beconnected to the search system 20 through network 58. The databases mayalso be split up so that they are located in multiple locales. Asdescribed above, index database 24 generally stores information aboutitems such as web sites which the search system 20 reviews, rather thanthe web sites themselves so that the search process is faster and moreefficient. The cached information database 26 generally stores copies orsub-copies of information about the items searchable by search system20. In addition, search system 20 may include system storage 74, whichstores the various components needed for the general operation of thesearch system 20, such as applications, system parameters, andinformation about registered users of the system.

Search system 20 communicates through an internal interface 54 and anexternal interface 52, which are shown as distinct interfaces, but maybe partially or fully combined, or may be represented by additionalinterfaces. By example, internal interface 54 may comprise interfacedevices for a high speed, high bandwidth network such as SONET,Infiniband, or Ethernet network cards, or any appropriate communicationhardware operating under an appropriate protocol, so that search system20 can respond to a large number of distinct requests simultaneously.External interface 52 may comprise interface devices for communicatingwith network 58, such as Ethernet network interface cards (NICs) orother communications devices. The precise design of the system 20 is notcritical to the proper operation of the invention, and could take anyappropriate form.

Within search system 20, a search engine 70 operates to produce searchresults in response to search requests from users, employing informationstored in index database 24. The information in index database 24 may begathered by crawler 76, which may continuously or almost continuouslyobtain new information from sources connected to network 58. Requests,such as search requests from users, may be received through externalinterface 52 and handled by request processor 66. For example, requestprocessor 66 may parse the requests and reformat them from html/textrequests to internally usable search terms/strings. The informationgenerated by search engine 70 in response to a request may also beconverted by response formatter 68 in a manner that allows it to be usedby the requesting device, such as in a WAP format, HTML document, XMLdocument, VoiceML result, etc., and then transmitted via externalinterface 52.

Predictive information may be retrieved and/or generated by predictivemodule 78, which may monitor requests from a user and responses to theuser. In this manner, the predictive module 78 is able to begin workingas soon as a request is received or a response is delivered. Forexample, where a web search request is received by the system 20, thatrequest may be processed and forwarded to search engine 70. In addition,the predictive module 78 may recognize the request, and cause additionalformatted requests to be forwarded to the search engine (e.g., newgroupssearches, image searches, or web blog searches using the same searchterms). The predictive module may cause the predictive information thatresults from those requests to be transmitted to the user's device, forexample, using response formatter 68.

The predictive module 78 may include, for example, predictive rules 84,prediction selector 82, and prediction engine 80. The predictive rules84 may include parameters that may be selected and changed so as tomanage the manner in which predictive information is gathered. The rules84 may be specific to particular users (e.g., in a profile of rules forthe user, or with pointers for a user to particular parameters so as tominimize storage space required). For example, the predictive rules 84for a particular user may indicate that the user prefers not to downloadimages, and that the user's expected bandwidth is a particular value.

Prediction selector 82 controls which prediction mode, of possibleoptions, is used by system 20. For example, prediction selector 82 maydirect that other search styles are performed as part of the predictiveprocess, or that web sites relating to hyperlinks returned as part ofthe initial response are gathered.

Prediction engine 80 applies processes to the predictive rules 84according to a setting indicated by the prediction selector 82.Prediction engine 80 thereby causes appropriate messages or requests tobe provided, for example, to the search engine 70 to retrieve additionalsearch results in different search styles, to direct the user's deviceto appropriate web sites so that it gathers predictive informationdirectly, or to forward information to the user's device.

The components of predictive module 78 thus cooperate to obtainpredictive information relating to, for example, requests from a user sothat the user's device can pre-fetch additional information the user islikely to request next. Although the predictive module 78 is shown ashaving three components as described above, the module 78 may take otherappropriate forms, with fewer or more components, and may be integratedinto system 20 in a manner other than that specifically shown. Also,similar predictive information services could be provided by a systemthat is not a search system, but instead allows users to receive othertypes of information and/or make other types of requests.

FIG. 3 is a schematic diagram of a wireless communication handset forgenerating requests and receiving and using information generated inresponse to those requests. A communication system 90 may be implementedin a device such as a personal communicator, e.g., a cellular telephone.The system 90 receives and transmits information wirelessly usingtransmitter 94, with the received signals being passed to signalprocessor 96, which may comprise digital signal processor (DSP)circuitry and the like. Normal voice communication is routed to or fromaudio processor 92, which may communicate with speaker/microphone 98,including via user interface 108.

User interface 108 handles all communication with the user of the system90, including voice, visual, and data entry communication. Visualpresentation of information may be provided via display screen 100.General data entry, apart from entered voice data, may occur throughkeypad 102, which may be arranged as a standard 12-key telephone keypad.The device may also be provided with appropriate control keys 104 forperforming necessary control functions. Key pad 102 and control keys 104may include contact push-buttons, joysticks, portions of touch-sensitivepanels, or other appropriate input devices. Although the communicationis shown for clarity as occurring through a single user interface 108,multiple interfaces may be used, and may be combined with othercomponents as necessary.

The system 90 may be provided with a number of computer applications116, such as games, applications to assist in dialing numbers, andapplications to permit web browsing, including the entry of data as partof the web browsing. The applications 116 may be stored in ROM, Flashmemory, RAM, MRAM, or otherwise, as appropriate, and may be accessed bythe system 90 as needed. A dialing module 112 may provide standarddialing functionality for the system, receiving entered dialing digitsor voice dialing instructions through interface 108, and providingappropriate dialing signals through transmitter 94 using communicationinterface 120.

A data entry module 110 receives data other than dialing instructions,such as search data entered into the system 90. The data entry module110 may provide the entered data directly to an application 116, or mayemploy a predictive module 118 to help gather additional predictiveinformation before a user requests it. The predictive module 118 maycomprise predictive rules 123, a prediction selector 121, and aprediction engine 122. These three components may work in a similarmanner to those discussed with respect to search system 20 in FIG. 2.The predictive module 118 may also be included as part of an application114. In general, the components allow all or part of the process ofidentifying and acquiring predictive information to be taken up by thesystem 90 itself rather than by a central system.

For example, where a user conducts a web search and the device is set sothat the prediction selector indicates that the content of additionalweb pages is to be pre-fetched, the prediction engine 122 may, uponreceiving all or part of the search results, begin issuing messages tothe web sites identified in the search result. Alternatively, if thesearch mode requires follow-up retrieval of information using differentsearch styles, the prediction selector 121 may cause the device not toobtain additional information, because such follow-up work is bestconducted by the server site, which can quickly run searches based onthe web, news, newsgroups, images, and other styles of searching.

Predictive storage 114 is simply an area of memory in which predictiveinformation may be stored, and then later provided as follow-upinformation. Predictive storage 114 may be dedicated memory, or may beone or more blocks of memory in a shared memory space. The predictivestorage 114 may comprise RAM, Flash memory, ROM, MRAM, or otherappropriate memory technologies. As noted above, the predictive storage114 may be organized in a FIFO manner, so that the oldest existinginformation is overwritten when the memory is full. The informationstored in predictive storage 114 may be accessed by applications 116,such as to provide follow-up information when a user requests it.

Although shown in an implementation in a personal communicator, system90 may take many other forms. For example, system 90 could beimplemented as part of a personal computer, whether networked orunnetworked, and if networked, whether by wire or wirelessly. Also, dataentry may occur in different manners, including by complete keyboard,constrained keyboard, or voice command. Also, one or more components maybe located remotely from the system 90, such as at a remote server, andthe functionality of system 90 may be provided by combining thecomponents or using components other than those shown.

FIG. 4 is a client-server flow chart showing exemplary steps forobtaining predictive information in response to a request. In general,the chart shows a process by which predictive information is obtainedwhile search results are being transmitted to a user, and by which thepredictive information is transmitted while the user is reviewing thesearch results. The flow chart generally depicts steps that can beperformed in parallel as an example of the operation of the process, butthe operations could occur in other orders and with other steps asappropriate.

At step 124, a search entry is received at a client device. This searchentry could comprise terms for which a web search is desired. The devicetransmits the search request at step 126, and a central system receivesthe request at step 128 and generates a search result at step 130.Again, the manner in which the search result is generated may occur inany of a number of appropriate manners, and is not critical to theoperation of the process. Once the search result is generated, it can betransmitted by the central system to the remote device (step 132), andbe received and displayed on the remote device (step 138).

Also, at or near the same time that the search result is being obtainedand/or transmitted, or at least before a user selects a substantialportion of the information represented by the search result, the centralsystem may obtain predictive information (step 134). At this step, allof the relevant predictive information may be obtained (e.g., allparallel search results on newsgroups, images, web blogs, local search,etc. may be performed) and transmitted to the remote device (step 136).Alternatively, part of the predictive information may be obtained, andthen transmitted, while other parts of the predictive information arebeing retrieved.

When the predictive information is received by the remote device, it maybe streamed into storage and a predictive indicator may be updated forthe user (step 142). For example, the predictive indicator may indicateto the user the progress of the download of potential follow-upinformation so that the user can keep reviewing the initial informationuntil they see that the follow-up information has been fully received.In general, the predictive information continues to be downloaded to theremote device until the download is complete or until there is aninterruption, such as by the presentation, by the user, or anothersearch request.

FIG. 5 is a flow chart showing exemplary steps for providing predictiveinformation in response to a request. The steps shown may generally beexecuted by a central server that receives requests, such as searchrequests, from remote users. At step 146, a search request is receivedfrom a user, and search results are obtained responsive to the requestat step 148. Simultaneously, or near simultaneously, the system maytransmit the results to the user (step 150) and begin obtainingpredictive information (step 152). When an appropriate amount ofpredictive information has been received, the system may begintransmitting the predictive information to the remote device (step 154),such as a part of the search results. The client application may then beconfigured in such a manner that it can appropriately store and displaythe information. The client may also make repeated individual requestsfor information, or the central system may simply transmit theadditional information to the same address to which it transmitted theprevious information (and the client device may keep “listening” forinformation). Also, when all identified information has been transmitted(step 156), the system waits (step 158), such as for follow-up actionsby the user that generate new demands on the central system.

If all identified information has not yet been transmitted (step 156),and no event has occurred to interrupt the transmission (step 160), thenadditional predictive information is identified, obtained, andtransmitted. If an interruption has occurred (step 160) and thatinterruption is caused by a new search request (step 162), the processmay begin again. If the interruption is not the result of a new searchrequest, such as when the interruption occurs as the result of anattempt to access pre-fetched information, the information may beprovided to the user (step 164). Of course, if the requested informationhas all been pre-fetched, it may simply be transmitted from memory,while if it has not been pre-fetched or fully pre-fetched, it can beobtained in whole or in part in a conventional manner. Once therequested information is downloaded, the system may return to pre-fetchany remaining predictive information.

FIG. 6 is a flow chart of exemplary steps for obtaining predictiveinformation relating to a request. In general, these steps may beperformed by a remote device from which a user is trying to accessinformation. At step 170, the device sends a search request to a systemat another remote site. The device then checks, at step 172, for thepredictive mode in which the device is set. If the predictive modedepends on the results returned for the request (step 174), then thedevice waits for the results (176) and then receives the results (step178).

The device may then begin gathering predictive information while theresults are displayed to the user. In step 180, the device applies thepredictive rules to ensure that the identification and gathering ofpredictive information is in accord with the user's preferences. Thedevice then determines what information is needed, based on applicationof the initial request or its response, and it transmits an informationrequest to obtain that information (step 182). After waiting for aparticular period of time, the information arrives at the device (step184), and the device may update its predictive indicator (step 186) toshow to the user how the download is progressing. If all of thepredictive information has been received (step 188), the device may waitfor additional input from the user (step 190). If all the information isnot received, the device may gather more predictive information.

FIGS. 7a-7c are exemplary screen shots showing the operation ofpredictive information retrieval apparatuses. FIG. 7a shows an exampleof a space-constrained display for a search engine offering. In theexample display, a search term has been entered and results have beenreturned in the form of a search result listing. The display also showstabs providing the options of applying the search request to othersearch styles. A gauge at the bottom of the display provides a generalindication of the amount of predictive information that has beendownloaded in the background while the user has reviewed the searchresults. As shown in the figure, a little less than one-half of thetotal predictive information has been downloaded. Thus, the user can seethat a request for follow-up information might or might not be answeredquickly. The gauge may also provide an indication of the amount ofremaining memory in the device or allocated for pre-fetched informationthat has been filled or not been filled.

FIG. 7b shows an exemplary screen shot with more detail, includingsearch results and an advertisement area. The advertisements may becreated to be relevant to the user by matching the search request, andmay be applied directly by the search service provider as part of thesearch result, or by a third party such as an advertising aggregator. Inthis figure, the progress of the background pre-fetching download isshown by the color of the tabs for each of the five pictured searchstyles: web, images, groups, news, and Froogle. For example, in thefigure, the web and news tabs are displayed in their reverse colors, soas to indicate that the predictive information relating to those tabshas been pre-fetched. From this display, the user is immediatelynotified that they could select the news tab, and the results would benearly immediately accessible.

FIG. 7c shows a search result display having a predictive indicator areaalong the left edge of the display. Specifically, the system isconfigured so that the predictive information to be retrieved includesdownloads of web pages from the locations signified by the informationin the search results. To aid the user in determining when thepredictive information for a particular search result is ready forreview, displays of progress for each group of results are shown, here,showing progress from 0% to 100%. In this manner, the user can know,specific to each result, how the gathering of information isprogressing. The displays for each search result may also provide a lessgraduated guide to download status, such as by showing a red dot next toa search result if its corresponding predictive information is not yetdownloaded, a green dot when it is downloaded, and perhaps a yellow dotwhen it is substantially downloaded (such that a user could select thefollow-up information and expect the last part of the follow-upinformation to be downloaded as the follow-up information is displayed,or at least before the user has reached the end of the follow-upinformation).

As used herein, the terms “electronic document” and “document” mean aset of electronic data, including both electronic data stored in a fileand electronic data received over a network. An electronic document doesnot necessarily correspond to a file. A document may be stored in aportion of a file that holds other documents, in a single file dedicatedto the document in question, or in a set of coordinated files.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the term “machine-readable medium” refers toany computer program product, apparatus and/or device (e.g., magneticdiscs, optical disks, memory, Programmable Logic Devices (PLDs)) used toprovide machine instructions and/or data to a programmable processor,including a machine-readable medium that receives machine instructionsas a machine-readable signal. The term “machine-readable signal” refersto any signal used to provide machine instructions and/or data to aprogrammable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back-end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front-end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back-end, middleware, orfront-end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theinternet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Theclient-server relationship need not fit a formal client-serverdefinition, however.

Although a few embodiments have been described in detail above, othermodifications are possible. Portions of this disclosure discussoperation though portable devices, but any of a number of devices may beused, including fully-functional general purpose computers. Also, thelogic flows depicted in the figures do not require the particular ordershown, or sequential order, to achieve desirable results. Also, othersteps may be provided, or steps may be eliminated, from the describedflows, and other components may be added to, or removed from, thedescribed systems. Other embodiments may be within the scope of thefollowing claims.

1-19. (canceled)
 20. A computer-implemented method, comprising:receiving, by a computing system, a query that was generated at a clientdevice; generating, by the computing system, a search result that isresponsive to the query; determining, by the computing system, predictedinformation that the client device would request in response to aselection of the search result by user input at the client device;identifying a first subset of the predicted information that has a firstcontent type; identifying a second subset of the predicted informationthat has a second content type, the second content type being differentfrom the first content type; and transmitting, by the computing system,the search result and the first subset of the predicted information tothe client device both (i) after the query has been received by thecomputing system and (ii) before the selection of the search result bythe user input at the client device, without transmitting the secondsubset of the predicted information to the client device before theselection of the search result by the user input at the client device.21. The method of claim 20, further comprising: receiving, by thecomputing system, a request that was generated at the client device inresponse to the selection of the search result by user input at theclient device; and transmitting, by the computing system and in responseto receiving the request, the second subset of the predicted informationto the client device.
 22. The method of claim 20, wherein: identifyingthe first subset of the predicted information comprises identifying textcontent of a web page that the client device would request in responseto the selection of the search result by the user input at the clientdevice; and identifying the second subset of the predicted informationcomprises identifying non-text content of a web page that the clientdevice would request in response to the selection of the search resultby the user input at the client device.
 23. The method of claim 20,wherein: identifying the first subset of the predicted informationcomprises identifying text content of a web page that the client devicewould request in response to the selection of the search result by theuser input at the client device; and identifying the second subset ofthe predicted information comprises identifying image content of a webpage that the client device would request in response to the selectionof the search result by the user input at the client device.
 24. Themethod of claim 20, wherein: identifying the first subset of thepredicted information comprises identifying text content of a web pagethat the client device would request in response to the selection of thesearch result by the user input at the client device; and identifyingthe second subset of the predicted information comprises identifyingvideo content of a web page that the client device would request inresponse to the selection of the search result by the user input at theclient device.
 25. The method of claim 20, wherein: the first contenttype is low bandwidth content; identifying the first subset of thepredicted information comprises identifying low bandwidth contentcontained in the predicted information; the second content type is highbandwidth content requiring a higher amount of bandwidth to transmitthan the low bandwidth content; and identifying the second subset of thepredicted information comprises identifying high bandwidth contentcontained in the predicted information.
 26. The method of claim 20,wherein: identifying the first subset of the predicted informationcomprises assigning a first prioritization value to the first subset ofthe predicted information; and identifying the second subset of thepredicted information comprises assigning a second lower prioritizationvalue to the second subset of the predicted information.
 27. The methodof claim 20, wherein the client device is configured to display thefirst subset of the predicted information after the selection of thesearch result by the user input at the client device prior totransmitting a request in response to selection of the search result bythe user input at the client device.
 28. The method of claim 27, whereinthe client device is further configured to: transmit the request inresponse to selection of the search result by the user input at theclient device; receive the second subset of the predicted informationafter transmitting the request; and display the second subset of thepredicted information along with the previously displayed first subsetof the predicted information.
 29. A non-transitory computer storagemedium encoded with a computer program, the program comprisinginstructions that when executed by data processing apparatus cause thedata processing apparatus to perform operations comprising: receiving,by a computing system, a query that was generated at a client device;generating, by the computing system, a search result that is responsiveto the query; determining, by the computing system, predictedinformation that the client device would request in response to aselection of the search result by user input at the client device; andidentifying a first subset of the predicted information that has a firstcontent type; identifying a second subset of the predicted informationthat has a second content type, the second content type being differentfrom the first content type; transmitting, by the computing system, thesearch result and the first subset of the predicted information to theclient device both (i) after the query has been received by thecomputing system and (ii) before the selection of the search result bythe user input at the client device without transmitting the secondsubset of the predicted information to the client device before theselection of the search result by the user input at the client device.30. The non-transitory computer storage medium of claim 29, theoperations further comprising: receiving, by the computing system, arequest that was generated at the client device in response to theselection of the search result by user input at the client device; andtransmitting, by the computing system and in response to receiving therequest, the second subset of the predicted information to the clientdevice.
 31. The non-transitory computer storage medium of claim 29,wherein: identifying the first subset of the predicted informationcomprises identifying text content of a web page that the client devicewould request in response to the selection of the search result by theuser input at the client device; and identifying the second subset ofthe predicted information comprises identifying non-text content of aweb page that the client device would request in response to theselection of the search result by the user input at the client device.32. The non-transitory computer storage medium of claim 29, wherein:identifying the first subset of the predicted information comprisesidentifying text content of a web page that the client device wouldrequest in response to the selection of the search result by the userinput at the client device; and identifying the second subset of thepredicted information comprises identifying image content of a web pagethat the client device would request in response to the selection of thesearch result by the user input at the client device.
 33. Thenon-transitory computer storage medium of claim 29, wherein: identifyingthe first subset of the predicted information comprises identifying textcontent of a web page that the client device would request in responseto the selection of the search result by the user input at the clientdevice; and identifying the second subset of the predicted informationcomprises identifying video content of a web page that the client devicewould request in response to the selection of the search result by theuser input at the client device.
 34. The non-transitory computer storagemedium of claim 29, wherein: the first content type is low bandwidthcontent; identifying the first subset of the predicted informationcomprises identifying low bandwidth content contained in the predictedinformation; the second content type is high bandwidth content requiringa higher amount of bandwidth to transmit than the low bandwidth content;and identifying the second subset of the predicted information comprisesidentifying high bandwidth content contained in the predictedinformation.
 35. The non-transitory computer storage medium of claim 29,wherein: identifying the first subset of the predicted informationcomprises assigning a first prioritization value to the first subset ofthe predicted information; and identifying the second subset of thepredicted information comprises assigning a second lower prioritizationvalue to the second subset of the predicted information.
 36. Thenon-transitory computer storage medium of claim 29, wherein the clientdevice is configured to display the first subset of the predictedinformation after the selection of the search result by the user inputat the client device prior to transmitting a request in response toselection of the search result by the user input at the client device.37. A non-transitory computer storage medium encoded with a computerprogram, the program comprising instructions that when executed by dataprocessing apparatus cause the data processing apparatus to performoperations comprising: generating, by a computing device, a query;transmitting, by the computing device, the query to a computing system;receiving, by the computing device and in response to transmitting thequery to the computing system: (i) a search result that is responsive tothe query, and (ii) a first subset of information that is for display inresponse to selection of the search result, the first subset of theinformation having a first content type; storing, by the computingdevice, the first subset of the first information in memory of thecomputing device; displaying, by the computing device and in response toreceiving the search result, the search result; receiving, at thecomputing device, user input indicating a selection of the searchresult; displaying, at the computing device, the first subset of theinformation both (i) in response to receiving the user input indicatingthe selection of the search result, and (ii) prior to receiving a secondsubset of the information from the computing system, the second subsetof the information having a second content type that is different fromthe first content type.
 38. The non-transitory computer storage mediumof claim 37, the operations further comprising: transmitting, at thecomputing device and in response to receiving the user input indicatingthe selection of the search result, a request for the second subset ofthe information to the computing system; receiving, at the computingdevice and after displaying the first subset of the information, thesecond subset of the information from the computing system; displaying,by the computing device, the second subset of the information along withthe first subset of the information.
 39. The non-transitory computerstorage medium of claim 38, wherein the first content type is textcontent and the second content type is non-text content.